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Finding Alphas: A Quantitative Approach to Building Trading Strategies

Book Description

Design more successful trading systems with this practical guide to identifying alphas

Finding Alphas seeks to teach you how to do one thing and do it well: design alphas. Written by experienced practitioners from WorldQuant, including its founder and CEO Igor Tulchinsky, this book provides detailed insight into the alchemic art of generating trading signals, and gives you access to the tools you need to practice and explore. Equally applicable across regions, this practical guide provides you with methods for uncovering the hidden signals in your data. A collection of essays provides diverse viewpoints to show the similarities, as well as unique approaches, to alpha design, covering a wide variety of topics, ranging from abstract theory to concrete technical aspects. You'll learn the dos and don'ts of information research, fundamental analysis, statistical arbitrage, alpha diversity, and more, and then delve into more advanced areas and more complex designs. The companion website, www.worldquantchallenge.com, features alpha examples with formulas and explanations. Further, this book also provides practical guidance for using WorldQuant's online simulation tool WebSim® to get hands-on practice in alpha design.

Alpha is an algorithm which trades financial securities. This book shows you the ins and outs of alpha design, with key insight from experienced practitioners.

  • Learn the seven habits of highly effective quants

  • Understand the key technical aspects of alpha design

  • Use WebSim® to experiment and create more successful alphas

  • Finding Alphas is the detailed, informative guide you need to start designing robust, successful alphas.

    Table of Contents

    1. Preface
    2. Acknowledgments
    3. About the WebSim™ Website
      1. WebSim™ Research Consultants
    4. PART I: INTRODUCTION
      1. 1 Introduction to Alpha Design
        1. How are Alphas Represented?
        2. How Does One Design an Alpha Based on Data?
        3. Quality of an Alpha
        4. Algorithm for Finding Alphas
      2. 2 Alpha Genesis – The Life-Cycle of a Quantitative Model of Financial Price Prediction
        1. Background
        2. Challenges
        3. The Life-Cycle of Alphas
        4. Data Input
        5. Predictive Output
        6. Evaluation
        7. Looking Back
        8. Statistics! = Statistical Arbitrage
        9. To Sum it Up
      3. 3 Cutting Losses
        1. How Do We Apply the Principle of the UnRule and of Cutting Losses?
        2. Summary
    5. PART II: DESIGN AND EVALUATION
      1. 4 Alpha Design
        1. Categorization of Alphas
        2. Development of an Alpha
        3. Value of an Alpha
        4. Practical Alpha Evaluation
        5. Future Performance
      2. 5 How to Develop an Alpha. I: Logic with an Example
        1. Step 1 → Collect Information
        2. Step 2 → Come Up with an Idea
        3. Step 3 → Translate into a Mathematical Expression
        4. Step 4 → Transform the Raw Expression by Applying Operations
        5. Step 5 → Final Robust Alpha
        6. Step 6 → Translate into Positions in a Financial Instrument
        7. Step 7 → Check for Robustness
      3. 6 How to Develop an Alpha. II: A Case Study
        1. Note
      4. 7 Fundamental Analysis
      5. 8 Equity Price and Volume
      6. 9 Turnover
        1. What is Turnover?
        2. Does that Mean Lowering Turnover Will Result in Lower Return?
        3. How Does Liquidity Factor into This?
        4. Does the Alpha Itself Play a Role?
        5. So What is the Right Turnover for an Alpha?
        6. Note
      7. 10 Backtest – Signal or Overfitting
        1. Backtest
        2. Overfitting
        3. How to Avoid Overfitting
      8. 11 Alpha and Risk Factors
      9. 12 The Relationship between Alpha and Portfolio Risk
        1. Portfolio Risk
        2. Alpha
        3. Less Beta, More Alpha
        4. Things to Remember
        5. Notes
      10. 13 Risk and Drawdowns
        1. Drawdowns
        2. Performance Measures for Risk and Anticipating Drawdown
        3. Summary
      11. 14 Data and Alpha Design
        1. How We Find Data for Alpha
        2. Data Validation
        3. Understand the Data before Using It
        4. Embrace the Big Data Era
      12. 15 Statistical Arbitrage, Overfitting, and Alpha Diversity
        1. Note
      13. 16 Techniques for Improving the Robustness of Alphas
        1. Simple Methods for Robustness Improvement
        2. Advanced Methods for Robustness Improvement
        3. Conclusions
        4. Note
      14. 17 Alphas from Automated Search
        1. It is a Good Idea to Make the Input Data Ratio-Like
        2. Input Data should not Come from too Many Categories
        3. It is not True that the Longer the Testing Period the Better
        4. Sensitivity Tests and Significance Tests are Important
      15. 18 Algorithms and Special Techniques in Alpha Research
        1. Boosting
        2. Digital Filtering
        3. Feature Extraction
    6. PART III: EXTENDED TOPICS
      1. 19 Impact of News and Social Media on Stock Returns
        1. News
        2. Social Media
      2. 20 Stock Returns Information from the Stock Options Market
        1. Volatility Skew
        2. Volatility Spread
        3. Options Trading Volume
        4. Options Open Interest
        5. Notes
      3. 21 Introduction to Momentum Alphas
      4. 22 Financial Statement Analysis
        1. The Balance Sheet
        2. The Income Statement
        3. The Cash Flow Statement
        4. Growth
        5. Corporate Governance
        6. Factor Analysis in Non-US Markets
      5. 23 Institutional Research 101
        1. Academic Research on Financial Markets – Needle Meets Haystack?
        2. Analyst Research
        3. Notes
      6. 24 Introduction to Futures Trading
        1. Commitment of Traders Report by the Commodity Futures Trading Commission
        2. Seasonality in Markets
        3. Risk On and Risk Off
        4. Contango/Backwardation
      7. 25 Alpha on Currency Forwards and Futures
        1. Key Market Features
        2. Underlying Factor Exposure
        3. Consequences of Instrument Grouping
        4. Basic Checklist for Alpha Testing
        5. Summary
    7. PART IV: NEW HORIZON – WEBSIM™
      1. 26 Introduction to WebSim™
      2. 27 Alphas and WebSim™ Fundamentals
        1. Alphas and WEBSIM™
        2. Alpha Sources
        3. Neutralization
        4. Universe
      3. 28 Understanding How WebSim™ Works
        1. Simulation Settings Panel
        2. Run Your First Alpha
        3. Another Sample Alpha
      4. 29 API Reference
        1. Available Market Data
        2. Available Operators
      5. 30 Interpreting Results and Alpha Repository
        1. Sharpe Ratio Bracket
        2. Simulation Results
        3. My Alphas Page
        4. Errors and Warnings
        5. Quick Recap
      6. 31 Alpha Tutorials
        1. Alpha Expression Examples
        2. How to Code Alphas in Python
        3. Python Alpha Examples
      7. 32 FAQs
        1. WebSim™
        2. Alpha Expressions, Market Data, and Functions
        3. Operations
        4. Alpha Performance
      8. 33 Suggested Readings
        1. Finance Basics
        2. Classical Papers for Quant Research
        3. Overfitting Risk and Where to Find Alphas
        4. Alpha Research Papers
    8. PART V: A FINAL WORD
      1. 34 The Seven Habits of Highly Successful Quants
    9. References
    10. Index
    11. EULA